A few days back an Entry Level Sales Representative came to me and asked “how do the managers allocate sales quota?” “And how do they know that their projections are right?” “What if the quotas aren’t met?”? Quite legit questions from someone who is trying to understand the trade game and perhaps preparing it well for the future, right?So here’s my answer:Traditionally, a Sales Manager would calculate the sales quota on the basis of the organization's annual sales goals, often determined by historical local knowledge rather than up-to-date facts. This could be in terms of units or amount.
So if company A wants to sell 1 Million units or make sales of $1 Billion in a year, the sales manager will divide the goals into small, achievable quotas and pass them onto the team members. Some team members would achieve their goals, and some wouldn’t and this would determine the company’s bottom line at the end of the fiscal year. Imagine a Sales Manager addressing the quota meeting with ``I've got a really good feeling about this year, so I’m going to add 20% more to everyone’s quotas so we can grow exponentially as a company”. How do you think this “Gut” feeling would pan out in the real world? Poorly allocated accounts, less effective and inconsistent results!
Perhaps in today’s dynamic sales world, where each and every business is now being dominated by technology and bombarded with various metrics to help them make informed decisions, Sales Managers are now heavily relying on advanced analytics solutions to improve planning and performance. In simple words, they are learning to embrace the valuable insights that help them improve sales strategy and decision-making. And they are thinking beyond revenue as a single KPI.
In fact, according to a survey by McKisey&Company of more than 1,000 sales organizations, it was found that 53 per cent of the participating organizations categorized as “high performing” were regular and effective users of sales and data analytics. But this doesn't mean that Managers should dip all their fingers and toes in the data lake, especially if the agenda is to optimize the ROI per sales rep. So what are the data points that one should consider which have a considerable impact on revenue? Let’s find out in this article.
This metric shows the total revenue by your teams against the goals set. Do you know how well your sales team is performing? How much are they contributing to the revenue or has it been flat for quite a while? Naturally, if everyone is hitting the specified sales target you would want to stick to the current plans and practices. But if your sales team is frequently missing their targets, poor quota setting and/or poor quota allocation could be the culprits here. Either way, you need the right analytics and technology solution to assess the team performance. Using Quota attainment effectively can help you find lagging indicators and streamline your efforts in the right direction. It can also help you better understand the sales talent and field behaviour. For example, if you notice that rep performance often drops during festival seasons or special occasions, you may need to rework a better incentive plan to encourage them to work harder during that period.
Average Deal Cycle
This metric shows the average amount of time between the first contact with customers and their final purchase. So how much time does it take for a sales rep to move from a prospective lead to a final sale? The average sales cycle metric is a useful benchmark for sales performance. It can also help create sales forecasts. Depending upon the nature of the product, a longer sales cycle may indicate efficiency problems that may need attention for better productivity. Generally, an average sales cycle would depend on various factors such as the customization requirements, inbound leads v/s outbound leads, and so on. Keeping all these factors in mind, a Sales Manager should segment the average sales cycle by prospect size, nature of the product and lead source. This will help see where exactly the prospective leads are stalling so corrective actions are taken without any loss of time.
Sales Compensation Data
This report helps analyze the effectiveness of sales compensation plans How do you measure the success of your compensation plans? A good compensation plan can really motivate sales teams to perform better. But as Sales Managers, we seldom ignore this aspect of the deal and rather focus more on other metrics which may or may not play a role as vital as an effective sales compensation plan. But when done right, sales compensation metrics can help Sales Managers understand the gap between the employee's expectations v/s the organizations'. Getting a clear picture of how much reps earn in comparison to their respective quotas can help Sales Managers understand the effectiveness of sales incentives. So for instance, if the performance of a sales rep usually drops off for a new product launch, it may be a strong indication that your compensation weighting is unbalanced and not driving the right behaviours.
Understanding Performance vs Tenure
This report provides transparency between the reps’ tenure and performance over a period of time. A sales rep with more than 5 years of experience at the organization ought to perform better than freshers? Well, that could turn out to be a myth if Sales Managers carefully studies analytics available at their disposal. If a Sales Manager finds anyone who doesn't perform according to their individual tenure standards, then it serves as a great indicator that the sales rep needs additional training to amplify their sales game.
For any sales organization, the difference between success and failure lies in the understanding of what works and what does not in a specified period of time. Once we have accurate data and insights on various elements of the business - ranging from customer acquisition to employee turnover and everything that lies in between, we can find ourselves in a better position to create sales performance strategies that not only benefit the sales department but an organization as a whole, and that’s exactly what analytics is all about!
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